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Efficient Lightweight Network with Transformer-Based Distillation for Micro-crack Detection of Solar Cells Read chapter Read first chapter

2024 | OriginalPaper | Chapter

A DNN-Based Learning Framework for Continuous Movements Segmentation

Authors : Tian-yu Xiang, Xiao-Hu Zhou, Xiao-Liang Xie, Shi-Qi Liu, Zhen-Qiu Feng, Mei-Jiang Gui, Hao Li, Zeng-Guang Hou

Published in: Neural Information Processing

Publisher: Springer Nature Singapore

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Abstract

This study presents a novel experimental paradigm for collecting Electromyography (EMG) data from continuous movement sequences and a Deep Neural Network (DNN) learning framework for segmenting movements from these signals. Unlike prior research focusing on individual movements, this approach characterizes human motion as continuous sequences. The DNN framework comprises a segmentation module for time point level labeling of EMG data and a transfer module predicting movement transition time points. These outputs are integrated based on defined rules. Experimental results reveal an impressive capacity to accurately segment movements, evidenced by segmentation metrics (accuracy: \(88.3\%\); Dice coefficient: \(82.9\%\); mIoU: \(72.7\%\)). This innovative approach to time point level analysis of continuous movement sequences via EMG signals offers promising implications for future studies of human motor functions and the advancement of human-machine interaction systems.

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Metadata
Title
A DNN-Based Learning Framework for Continuous Movements Segmentation
Authors
Tian-yu Xiang
Xiao-Hu Zhou
Xiao-Liang Xie
Shi-Qi Liu
Zhen-Qiu Feng
Mei-Jiang Gui
Hao Li
Zeng-Guang Hou
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Neural Information Processing

Print ISBN: 978-981-9980-66-6

Electronic ISBN: 978-981-9980-67-3

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-99-8067-3_30

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